www.nat-hazards-earth-syst-sci.net/11/1293/2011/ doi:10.5194/nhess-11-1293-2011
© Author(s) 2011. CC Attribution 3.0 License.
and Earth
System Sciences
Probabilistic assessments of climate change impacts on durum
wheat in the Mediterranean region
R. Ferrise1, M. Moriondo1, and M. Bindi2
1IBIMET-CNR, Florence, Italy
2DiPSA, University of Florence, Florence, Italy
Received: 23 December 2009 – Revised: 31 March 2010 – Accepted: 8 April 2011 – Published: 9 May 2011
Abstract. Recently, the availability of multi-model ensem-ble prediction methods has permitted a shift from a scenario-based approach to a risk-scenario-based approach in assessing the ef-fects of climate change. This provides more useful infor-mation to decision-makers who need probability estimates to assess the seriousness of the projected impacts.
In this study, a probabilistic framework for evaluating the risk of durum wheat yield shortfall over the Mediter-ranean Basin has been exploited. An artificial neural net-work, trained to emulate the outputs of a process-based crop growth model, has been adopted to create yield response sur-faces which are then overlaid with probabilistic projections of future temperature and precipitation changes in order to estimate probabilistic projections of future yields. The risk is calculated as the relative frequency of projected yields be-low a selected threshold.
In contrast to previous studies, which suggest that the beneficial effects of elevated atmospheric CO2concentration
over the next few decades would outweigh the detrimental effects of the early stages of climatic warming and drying, the results of this study are of greater concern.
1 Introduction
Durum wheat is a rain-fed crop that is widely cultivated over the Mediterranean Basin. The major climatic constraints to durum wheat yield in Mediterranean environments are high temperatures and drought, frequently occurring during the crop’s growth cycle (Porter and Semenov, 2005; Garc´ıa del Moral et al., 2003). As a consequence, projected cli-mate changes in this region, in particular rising temperatures and decreasing rainfall (Gibelin and D´equ´e, 2003), may
se-Correspondence to: R. Ferrise (roberto.ferrise@unifi.it)
riously compromise durum wheat yields, representing a se-rious threat to the cultivation of this typical Mediterranean crop.
Crop growth models have been widely used to evaluate crop responses (development, growth and yield) to climate change impact assessments by combining future climate con-ditions, obtained from General or Regional Circulation Mod-els, with simulations of CO2physiological effects, derived
from crop experiments (see Downing et al., 2000; Ainsworth and Long, 2005). Since future scenarios typically have no as-sociated likelihood, to investigate the uncertainties in future impacts, the effects of climate change are usually estimated over a number of scenarios representing a range of uncertain-ties as realistic as possible (Mearns et al., 2001).
Recently, the availability of multi-model ensemble predic-tion methods (e.g. Murphy et al., 2007; Tebaldi and Knutti, 2007) has permitted the assignment of likelihoods to fu-ture climate projections. This has allowed a move from the scenario-based approach to a risk-based approach in assess-ing the effects of climate change (New et al., 2007), pro-viding more useful information to decision-makers, who, as reported by Schneider (2001), need probability estimates to assess the seriousness of the projected impacts.
probabilistic representation of projected changes in the same climatic variables may be overlaid.
This approach has been exploited in the ENSEMBLES FP6 EU Project, which aims to assess the climate change impact on typical Mediterranean crops. This study focuses on the assessment of the risk of durum wheat (T. turgidum L. subsp. durum (Desf.) Husn) yield falling below fixed thresh-olds in the Mediterranean Basin.
2 Materials and methods
To perform the probabilistic assessment of climate change impact on durum wheat yield, the procedure adopted in-volved the following steps:
1. Calibration and validation of a mechanistic durum wheat crop growth model.
2. Simplification of the mechanistic model using an Arti-ficial Neural Network (ANN) to link the model outputs to some key climatic and management variables. 3. Creation of impact Response Surfaces (RSs) for durum
wheat over the case study area, altering the baseline cli-mate with respect to key climatic variables (e.g. Tem-perature (Temp), and Precipitation (Prec), CO2).
4. Assessing the risk of yield shortfall using future prob-abilistic projections for the A1B scenario provided by the Met-Office Hadley Centre.
5. Generation of maps of future risk of durum wheat yield shortfall.
2.1 Durum wheat impact model calibration and validation
The Sirius Quality v1.1 model (SIRIUS) was calibrated and validated to simulate winter durum wheat yield. SIRIUS is a wheat simulation model that calculates biomass produc-tion from intercepted photosynthetically active radiaproduc-tion and grain growth from simple partitioning rules (Jamieson et al., 1998). The model accounts for the enhanced CO2effect by
linearly increasing radiation use efficiency (RUE), so that for a doubling of CO2air concentration the RUE is increased by
30% (Jamieson et al., 2000). A simple soil sub-model is used for the dynamics of water and nitrogen (N) in the ground. As inputs, the model needs daily weather data consisting of min-imum temperature (Tmin), maximum temperature (Tmax), Prec and global radiation (Rad). It allows the user to specify management parameters such as the sowing date, cultivar ge-netic coefficients (e.g. photoperiodic sensitivity, duration of grain filling, minimum and maximum potential leaf numbers, etc.), soil profile properties (e.g. soil hydrological properties, thickness, initial water and nitrogen content, etc.), fertilizer and irrigation management, and atmospheric CO2
concentra-tion.
The SIRIUS model, originally developed for bread wheat, was calibrated to reproduce a general winter durum wheat crop with a medium growing cycle and few vernalization re-quirements. The calibration was performed using data from two open-field experimental trials carried out in Florence (Italy, 11.11◦E, 43.3◦N) in 2003–2005 (Triossi, 2006). The
thermal time of the main phenological phases (e.g. from sow-ing to emergence and grain-fillsow-ing duration), plant day-length response (leaf production per hour of daylight), minimum and maximum leaf numbers, and phyllochrone and vernali-sation parameters were the variables calibrated according to experimental evidence.
The accuracy of the calibrated model in reproducing the observed yields was tested on both the local and regional scale to ensure its general applicability. On the local scale, phenological and yield data from 3 sites located in the north (Milano, 9.3◦E, 45.4◦N), middle (Roma, 12.2◦E, 41.8◦N)
and south (Foggia, 15.7◦E, 41.5◦N) of the Italian peninsula
were used as an independent data set. Yield and phenological data, soil properties and crop management data (e.g. sowing date, amount and splitting of N fertilisation) were provided by the Istituto Sperimentale per la Cerealicoltura for the pe-riods 1999–2003 for Milano and Roma and 2000–2004 for Foggia. For each site, daily values ofTmin,Tmax, Prec and Rad were obtained by the Italian Meteorological Service.
In order to analyse the performance of SIRIUS in sim-ulating yield on the regional scale, the model was run for a selected number of places evenly distributed across the Mediterranean Basin. The outputs of the model were com-pared with the observed yield data for durum wheat, obtained by the Statistical Office of the European Commission. This dataset has a different time series according to the country and the crop considered: in general, longer time series were available from 1989 to 2004, but in some cases they were limited to 6 yr.
A complete series of observed 50 km daily interpolated meteorological data (Tmin,Tmax, Prec and Rad), extracted from Monitoring Agricultural ResourceS (MARS) of the Joint Research Centre (JRC) archive (MARS, 2009), was processed and adapted to the requirements of the weather files managed by the SIRIUS model.
Since crop management practices are not retrievable on such a spatial scale, a climatic criterion was adopted to iden-tify the optimum sowing date: sowing was matched with the point when the mean temperature fell below 13◦C for 5
con-secutive days and rainfall was<2 mm in the period between 1 October and 15 February. Nitrogen fertilization was set at 100 kg N ha−1, split in two applications: 1/3 during tillering
2.2 Emulating the SIRIUS model outputs
To reduce the computing time needed to apply the procedure over such a wide area as the Mediterranean Basin and for sev-eral time slices, an ANN was adopted to simplify the SIRIUS model.
The ANN is a computer system based on several sim-ple and highly interconnected processing elements similar to the neural architecture of the human brain (McClelland and Rumelhart, 1986) and it was adopted to exploit the non-linear relationship between predictor variables (climate, soil, man-agement) and SIRIUS outputs. The ANN approach is being used in several disciplines (Widrow et al., 1994) because of its ability to perform non-linear modelling without a priori knowledge about the relationships between input and output variables (Bindi and Maselli, 2001; Zhang et al., 1998).
The ANN structure adopted was a multilayer perceptron (MLP) with a feed-forward configuration. This structure, se-lected because of its capacity to solve climate problems, has been well demonstrated in several previous studies (Gardner and Dorling, 1998; Trigo and Palutikof, 1999). More specif-ically, in this study, an ANN-MLP structure with three lay-ers and 20 hidden nodes was selected. A non-linear transfer function (log-sigmoid) was selected for all nodes and layers, and a back-propagation algorithm (Rumelhart et al., 1986) was used for training the ANN. The optimal number of hid-den nodes (over a range of 5–25 with a 5-node step) and the correct learning rate and momentum were determined by car-rying out a sensitivity analysis (Moriondo and Bindi, 2006).
The ANN was trained using the outputs from SIRIUS ob-tained for a wide range of climate, soil and management practices in order to reproduce yield variability over the Mediterranean domain. Daily climatic data for Temp, Prec and Rad for a 30-year period (1975–2005) were extracted from the MARS-JRC archive. Nine grid cells, representa-tive of the climatic variability of the Mediterranean Basin, with particular attention to the winter and spring regime of Temp and Prec, were first selected. For this purpose, all grid points in the study area were grouped with respect to annual, winter and spring total Prec and mean Temp, using K-means cluster analysis. Within each group, one grid point was ran-domly selected to perform a scenario sensitivity analysis by changing daily Temp and Prec from−2◦C to +10◦C (2◦C
step) and from –60% to +40% (20% step), respectively. The selected grid cells and the central values of Temp and Prec of the corresponding class resulting from the cluster analysis are summarized in Table 1.
For each combination of Temp and Prec changes, the SIR-IUS model was run for 5 different CO2 concentration
lev-els (from 350 ppm to 750 ppm, with 100 ppm step), 3 differ-ent soil types (Table 2) and 3 N fertilization rates (110, 170 and 230 kg N ha−1). For each of the 17 010 resulting
com-binations (7 Temp×6 Prec×5 CO2×3 soils×3 N
fertiliza-tion rates×9 grid cells), the average grain yield over the
30-year period was calculated from the output of the SIRIUS model and used as predictand variables for training the ANN. The predictor variables for the ANN training were selected considering the approach proposed by Olesen et al. (2007). Five input variables were selected to take into account the effects of soil, crop management and climate conditions and used as predictors to train the ANN over the 9 grid points:
– Soil water content (SWC), calculated as the difference between field capacity and wilting point, in mm – Nitrogen fertilization rate, in kg N ha−1
– CO2concentration, in ppm
– T (AMJ): mean spring temperature (April, May, June)
– P (AMJ): cumulated spring rainfall.
At the onset, data for all seasons were considered as predic-tors, but those related to spring had the best predicting perfor-mances. Dropping the variables relevant to winter, summer and autumn did not affect the predictive accuracy of the ANN and at the same time increased ANN computation efficiency during the training phase.
As with any other statistical model, ANNs should gener-ally be trained (calibration) and tested (validation) using 2 independent data sets. In this paper, two different strategies were used to derive the subsets for calibration and validation, namely simple validation and cross validation. In the first case, the ANN was trained using a random selection from the initial dataset (80%) while the remaining 20% was used as independent data to test its efficiency. The second valida-tion strategy consisted of a Leave-One-Out cross-validavalida-tion test in which one site in turn was omitted from the training subset and used to test the ANN efficiency. In order to avoid over-fitting problems, both strategies included internal test-ing durtest-ing the calibration phase, which was performed on 20% of the training subset used as independent data.
The accuracy of the SIRIUS and the ANN model in simu-lating durum wheat yield was calculated using: (1) root mean square error (RMSE); (2) mean absolute error (MAE) and (3) Pearson’s correlation coefficient.
2.3 Case study area and impact response surfaces
The domain included in the window 34.2◦– 46.0◦Lat N and
−9.5◦– 28.2◦Lon E was selected as the case study area to
perform the probabilistic assessment of climate change im-pacts on winter durum wheat yield over the Mediterranean Basin.
The baseline climate for this area was obtained from the ENSEMBLES E-OBS dataset (Haylock et al., 2008), which consists of 25 km interpolated daily observed data for Prec,
Table 1. Location of the nine grid points selected to perform the SIRIUS sensitivity analysis for producing the ANN training set and central values of annual, winter and spring temperatures (◦C) and precipitation (mm) of the corresponding climate class.
Region Latitude Longitude Altitude Temperature Precipitation
Annual Winter Spring Annual Winter Spring
Southern Italy (Sicily) 37.24 14.91 271 17.1 10.4 18.6 429 163 50
Turkey (Aydin) 37.51 27.54 273 17.1 10.7 18.5 625 231 85
Central Greece (Thessalia) 39.29 22.11 304 15.7 9.1 17.5 521 144 104
North-eastern Spain (Aragon) 41.12 0.34 312 16.1 9.9 17.7 360 92 88
Central Italy (Lazio) 41.87 12.31 60 15.9 9.6 17.3 827 252 133
North-western Spain (Galicia) 43.27 −8.13 316 14.2 9.8 15.0 1592 453 322
Central France (Midi-Pyrenees) 44.43 2.38 517 13.4 7.8 15.0 1047 263 239
North-western Italy (Piedmont) 44.62 8.05 349 12.7 5.8 15.4 869 172 227
North-eastern Italy (Veneto) 45.05 11.23 10 13.9 7.4 15.9 690 155 163
Table 2. Characteristics of soils used to train the ANN. Soil Wa-ter Content (mm) calculated as difference between waWa-ter content at field capacity and at wilting point for 1m depth.
Soil class Sandy Loamy Silty
Sand % 92 37 7
Clay % 5 20 8
Field Capacity (vol%) 13 27 31
Wilting Point (vol%) 6 12 10
Soil Water Content (SWC) 70 140 210
wheat is not usually cultivated, were excluded from the fol-lowing analyses, as well as a few zones of the Italian Penin-sula (e.g. Sardinia, Sicily and Calabria) and Southern Greece, for which there is a lack of weather data on the present and future climate.
Next, the baseline climate was then calculated on a grid-cell basis as the monthly average ofTmin, Tmax and cu-mulated Prec over the period 1961–1990. For each grid cell of the domain, the trained ANN was used to create the du-rum wheat yield RSs for future periods (see below). The RSs were created for the different time slices using the baseline climate perturbed over a range of−2◦C to +10◦C for annual
Temp and−60% to +40% for annual Prec, and assuming no seasonal variations in the future climate pattern. For each future time slice, the CO2air concentration level was set
ac-cording to the A1B SRES emission scenario (Naki´cenovi´c and Swart, 2000). SWC and N fertilization were consid-ered as constant for all the grid points and set at 125 mm and 170 kg N ha−1, respectively.
2.4 Future climate probabilistic projections and climatic risk
Probabilistic projections of future climate changes were pro-vided by the Met-Office Hadley Centre (MOHC) as joint probability distribution functions (PDFs) for annual sur-face temperature change and annual percentage precipitation change for the A1B scenario with respect to the 1961–1990 baseline period. The PDFs provide a measure of the uncer-tainty of the future climate and represent changes in the 20-year average climate for decadal steps for the periods 2010– 2030 to 2080–2100, calculated for the 2.5◦latitude by 3.75◦
longitude HadCM3 grid boxes covering the whole of Europe (Harris et al., 2010).
In this study, the PDFs were employed as 10 000 ran-dom, equally-likely projections of the same variables sam-pled from the PDFs. The use of 10 000 points is a good com-promise to represent the PDF, but may not give a particularly smooth picture of it.
The PDFs of each grid cell were overlaid on the rele-vant RSs calculated for each 25 km-spaced grid cell of the ENSEMBLES E-OBS dataset to produce the corresponding probabilistic distributions of future yields.
Table 3. Results of SIRIUS validation at regional scale. The Pearson’s coefficient was 0.88 and RMSE 0.46 Mg ha−1.
Regions Latitude Longitude Observed yield (Mg ha−1)
Simulated yield (Mg ha−1)
Southern France 43.65 4.96 3.26 3.62
Northern Italy 45.48 11.88 4.88 4.53
Central Italy 42.34 11.73 2.94 3.58
Southern Italy 37.78 12.68 2.17 2.21
Central Greece 39.73 22.19 3.02 2.83
Southern Spain 36.83 −5.34 2.57 3.32
3 Results
3.1 Calibration and validation of SIRIUS and ANN models
On the local scale, SIRIUS was able to adequately simu-late both the phenology and yield of durum wheat in all three locations. In particular, Pearson’s correlation coef-ficient between the observed and simulated values turned out to be very high for both anthesis date (r=0.87) and yield (r=0.87), whilst the overall RMSE of simulations was 3.3 days and 0.371 Mg ha−1 for anthesis and yield
respec-tively.
On the regional scale, the ability of the model to repro-duce yields was poorer. When considering all the valida-tion sites and years, the Pearson’s correlavalida-tion coefficient and RMSE were 0.66 and 0.97 Mg ha−1respectively. The greater
uncertainty of the model on the regional scale may be as-cribed to the large geographical area and the limitations of the observed data in fully characterizing spatial variability. For instance, observed yields are largely influenced by man-agement strategies and soil conditions, which vary by farm and region, and which are difficult to measure and include in process-based crop models on this scale (Reidsma et al., 2009).
Importantly, the comparison between the observed and simulated average yields (Table 3) indicates that the model captured the spatial variability of yield very well (0.88 and 0.46 Mg ha−1, Pearson’s coefficient and RMSE,
respec-tively) and was thus held to be trustworthy in reproducing the spatial difference in crop yield for present and future climate conditions.
The test analyses showed a high level of correspondence between the SIRIUS outputs and the ANN simulated yields, indicating the robustness of the proposed approach in em-ulating the results of the mechanistic model. In particular, the Pearson’s coefficients for the simple and cross validations were over 0.94, whereas the RMSE and MAE were just be-low 0.47 Mg ha−1and 0.37 Mg ha−1, respectively.
In any case, the RMSEs calculated for grain yield by both the SIRIUS and ANN models were comparable to those
Fig. 1. Spatial distribution of present-day (1961–1990) mean tem-perature over the period April, May, June (a), and 50th percentile of the probability distribution of temperature change projected for 2010–2030 (b) and 2070–2090 (c).
reported for the main wheat models for bread wheat (David and Jeuffroy, 2009)
3.2 Baseline and future climate over the study area
The picture for the baseline period indicates that mean T(AMJ) over the study area ranged from 10.9◦ to 19.6◦
Fig. 2. Spatial distribution of present-day (1961–1990) cumulated precipitation over the period April, May, June (a), and 50th per-centile of the probability distribution of percentage precipitation change projected for 2010–2030 (b) and 2070–2090 (c).
median temperature changes vary from 1.6◦C in 2010–2030
(Fig. 1b) to about 5◦C at the end of the century (Fig. 1c). The
greatest increases within the study area were predicted for the southern areas of central Europe, while the smallest in-creases were projected for the eastern regions of the domain and Galicia and Western France. The range of uncertainty varied substantially with the time-distance of future periods and rose to 4.4◦C, on average, by the end of the century.
The mean P(AMJ) showed a clear latitudinal pattern, with lower values at lower latitudes (Fig. 2a). The greatest precip-itation were recorded in Galicia, the Pyrenees and the North-ern Balkans region, with more than 300 mm, whereas south-ern Italy and the south-eastsouth-ern areas showed the driest cli-mate with less than 100 mm of rainfall. As regards the future climate, a progressive decrease in precipitation was projected over the whole study area (Fig. 2b, c). Over the next decades, the decrease will be moderate and in some northeastern re-gions a slight increase is expected, but at the end of the cen-tury the reduction will be widespread and more severe in the central Iberian Peninsula and Greece (more than 20% reduc-tion). Also for precipitation, the range of uncertainty will increase as the century progresses and for many grid boxes there are significant probabilities of both drier and wetter cli-mates.
3.3 Simulated durum wheat yield under present-day climate conditions
The simulated durum wheat yield for the baseline period ranges from 2.9 to 7.2 Mg ha−1 with a clear latitudinal
gra-dient. The highest yields are estimated in Southern France and Galicia, with more than 6.5 Mg ha−1 (Fig. 3). Higher
yields were also simulated in north-eastern areas of the case study area with a layer in north-western and central Italy, while smaller yields, down to 3.5 Mg ha−1, were estimated
for southern areas of the Mediterranean Basin. These results are in agreement with other European-scale assessments of the productivity of wheat (Harrison et al., 1995), once more confirming the robustness of the ANN procedure in simpli-fying SIRIUS, allowing adequate reproduction of the spatial variability of yield over a wide area, but with much comput-ing time savcomput-ing.
3.4 Risk over the study area
In contrast to previous studies which suggest that the benefi-cial effects of elevated atmospheric CO2concentration over
the next few decades would outweigh the detrimental effects of the early stages of climatic warming and drying (e.g. Ole-sen and Bindi, 2002; Parry et al., 2004), the results of our analysis bring more concern (Fig. 4a). Early in the next decades, the risk of reductions in yield below the long term yield average is quite high and widespread. As the century progresses, for a large part of the study area the risk still in-creases, reaching its maximum by mid century (2050–2070, Fig. 4b,c). In the last period (2070–2090, Fig. 4d), the risk slightly decreases with respect to the previous time slice with the effect of larger uncertainty in climate projections sim-ulated for the end of the century. In some regions, for in-stance, southern Portugal (grid cell centroid−7.5 lon E, 37.5 lat N) Prec in 2050–2070 is expected changing in the range of +9% to−42% (10th and 90th percentile respectively; data
not shown). In the same area, at the end of the century Prec may range +28 to−43%, which slightly decreases the risk,
given the effect of the positive impact of higher precipitations on yield.
Fig. 3. Spatial distribution of the simulated 30-year mean durum wheat yield for the reference period (1961–1990) over the case study area.
Fig. 4. Spatial plots of risk of durum wheat yield shortfall by: (a) 2010–2030, (b) 2030–2050, (c) 2050–2070 and (d) 2070–2090. The risk was calculated as the relative frequency of future projected yields that do not overcome the selected threshold (30-year mean yield estimated from the baseline).
4 Discussion
The probabilistic projections provided by the MOHC are in accordance with other ensembles analyses based on the most advanced sets of global and regional climate model simu-lations, giving a collective picture of substantial drying and warming of the Mediterranean region (Giorgi and Lionello, 2008). This trend is expected to greatly affect the perfor-mance of rain-fed crops, such as durum wheat. Higher tem-peratures, which increase the crop development rate, shorten the crop growing cycle, thus reducing the time for biomass accumulation. A decreasing rainfall rate may result in in-creasing crop water stress. On the other hand, enhanced CO2
concentration, increasing the efficiency in the use of both wa-ter and radiation, is expected to reduce the impact of climatic change on yield arising from a warmer and drier climate (Gi-annakopoulos et al., 2009; Olesen and Bindi, 2002; Kimball et al., 2002).
These phenological and physiological responses have been implicitly taken into account during the training of the ANN, which was based on the SIRIUS simulations as driven by all the possible combinations of Temp and Prec changes en-compassing the prospected changes in climate (changes from
−2◦C to +10◦C and from−60% to +40% in Temp and Prec,
respectively).
part of the growing season (see Garc´ıa del Moral et al., 2003). Nevertheless, using mean annual changes of Temp and Prec, we have assumed no seasonal variations in the pattern of the future climate, therefore the results may not necessarily hold true if the future seasonal distributions of the climatic vari-ables are greatly different from those employed in training the ANN.
The response surfaces approach avoids the very large num-ber of model runs needed to estimate the impact likelihoods directly from the probabilistic climate projections. In fact, to create the impact response surfaces, the model runs were re-stricted to a number that was sufficient to cover the likely range of prospected future changes. Once the response surfaces had been constructed, the PDFs describing future changes in the appropriate variables could be superimposed to provide PDFs of the impact to compare with the threshold selected to define the risk related to climate change.
Nevertheless, the methodology may present some limita-tions. The major disadvantage of the RSs approach is that it may not be appropriate when impact models are critically dependent on several variables, requiring the construction of RSs in multi-dimensional space. In this case, the number of model runs necessary to cover all the possible combinations of the variables selected increases exponentially and the in-terpretation of the resulting RSs may be greatly complicated. On the other hand, constructing two-dimensional impact RSs necessarily requires a simplification of the model or an as-sumption about the relationship of the two selected variables with respect to all the variables explaining the phenomenon analysed.
In this paper our analysis has focused on the risk of yield shortfall related to climate change and other sources of un-certainty have not been evaluated. In particular those related to (1) the impact model, (2) the statistical model to simplify the impact model and (3) the future values of the explanatory variables other than Temp and Prec require further consider-ation.
Crop growth models, although mechanistic, may contain many simple empirically-derived relationships that do not completely represent actual plant processes. Several aspects, such as weeds, pests, extreme climate events and soil condi-tions (i.e. salinity or acidity) are not considered or scarcely controlled for, representing another source of uncertainty.
Uncertainty increases when statistical models are used to simplify the process-based impact model. As reported by Iglesias et al. (2009), using statistical models to represent process-based crop responses accounting for the most impor-tant environmental and management variables allows an easy expansion of the analysis over large areas, but only partially explains causal mechanisms, and introduces another source of uncertainty due to simplifications or assumptions of how the explanatory variables can be related to the full set of re-quired input variables.
Exploring such uncertainties, considering the adoption of several impact models as well as different statistical models in the assessment of risk, would be an interesting topic for future work.
The risk observed in a business-as-usual scenario (i.e. us-ing common crop management, medium growus-ing cycle cul-tivars, medium SWC, no irrigation) may be reduced by us-ing adaptation strategies to contrast the negative effects of a warmer and drier climate. Changes in crop characteristics or crop management aiming to decrease crop exposure to water and heat stress and lengthen the crop growing cycle, should be considered (Iglesias et al., 2009).
Accordingly, strategies for adapting to climate change should concentrate on the use of drought-tolerant cultivars, increasing water-use efficiency, and better matching phenol-ogy to new environmental conditions. The shortening of the growth cycle is a noticeable yield-reducing factor and the selection or use of cultivars with a longer cycle may be suggested as a way of compensating for the reduced time they have for biomass accumulation under warmer condi-tions (Tubiello et al., 2000). In the Mediterranean region, cultivars with an earlier anthesis may be selected, as this will allow the grain-filling period to occur in cooler and wetter periods, avoiding summer drought and heat stress. Man-agement practices promoting advanced phenological stages, such as earlier sowing, may be adopted as well (Moriondo et al., 2010). Enhanced drought tolerance should be the charac-teristic most desired in a typical rain-fed crop of the Mediter-ranean basin, but other strategies should be considered, such as the application of irrigation in the crop-growth stages that are more sensitive to water stress (Zhang and Oweis, 1999), or deep ploughing to increase the available water content of the soil.
5 Conclusions
In this work we have presented a novel approach to climate change impact assessment in agriculture. This approach has permitted us to evaluate climate change impacts in terms of risk over the wide area of the Mediterranean Basin, with great computing-time saving. The methodology, although intro-ducing additional uncertainty, has allowed us to assess the risk of crop shortfall related to climate change by assigning probability estimates of the impact instead of providing only uncertainty ranges of the possible impact, with no informa-tion on relative likelihoods.
The results of this analysis, in contrast to previous stud-ies, indicate that the projected warmer and drier climate over the Mediterranean basin will increase the risk of yield loss, while the positive effects of increasing CO2 are not able to
completely counterbalance this trend. In a few areas, at the northern latitudes of the domain, the risk is lower, as the re-sult of the combined effect of non limiting precipitation and increased CO2. Uncertainties in future climate projections
progressively increase up to the end of the century, resulting in a reduction of risk.
For a more comprehensive analysis of the risk, further works are needed to take into account the other sources of uncertainty and investigate the possible adaptation strategies needed to cope with future climate change.
Acknowledgements. The authors thank their colleagues for their continuing support and discussion in coffee breaks. Special thanks go to Giacomo “Jack” Trombi and Camilla Dibari for their help in producing figures and their GIS support, respectively.
Edited by: J. E. Olesen
Reviewed by: three anonymous referees
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